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Record W2016348254 · doi:10.1097/ans.0b013e31828077eb

Barriers to Mental Health Care

2013· article· en· W2016348254 on OpenAlexafffund
Patricia Lingley‐Pottie, Patrick J. McGrath, Pantelis Andreou

Bibliographic record

VenueAdvances in Nursing Science · 2013
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsBeatrice Hunter Cancer Research InstituteNova Scotia Health AuthorityCapital District Health Authority
FundersCanadian Institutes of Health Research
KeywordsIntervention (counseling)Clinical psychologyScale (ratio)PsychologyMedicineMental healthPsychiatry

Abstract

fetched live from OpenAlex

Treatment barriers have prompted the development of new models of care. Distance delivery systems bridge the access gap, increasing service availability. Understanding differences between systems can inform system improvements. Sixty participants from the Strongest Families telephone intervention for child behavior difficulties participated. Participants completed a questionnaire to explore differences in perceived treatment barriers (Treatment Barriers Index-TBI) and therapeutic processes (eg, therapeutic alliance, self-disclosure, health outcome) between face-to-face versus distance treatment. The TBI scale has strong internal reliability (Cronbach α: 0.95 [face-to-face]; 0.90 [distance]). Statistically significant differences were found between delivery system TBI mean scores, indicating fewer barriers with distance treatment. Therapeutic process differences between delivery modes suggest enhanced therapeutic alliance and self-disclosure scores with distance treatment. Increased access, convenience, and sense of privacy (visual anonymity) offered by a distance delivery system may provide an enhanced experience for some individuals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.446
Teacher spread0.435 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2013
Admission routes2
Has abstractyes

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